Konstantinidi et. al. state in their broad survey of Volatility-Index forecasting: "The question whether the dynamics of implied volatility indices can be predicted has received little attention". The overall result of this and the quoted papers is: The VIX is too a very limited extend (R2 is...

The existing literature, in both theoretical and empirical viewpoints, indicates that there is no consensus regarding the effects of exchange rate volatility on bilateral trade flows. It can show the different effects across countries and industries. This article examines impact of volatility of...

Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the...

A variety of historical-volatility, peer-historical-volatility, implied-volatility and blended estimators of stock price volatility are developed and tested for a group of large U.S. companies over roughly a thirty-year window. Longer-term historical estimators (up to fifteen years) are found to...

We analyse the importance of jumps and the leverage effect on forecasts of realized volatility in a large cross-section of 18 international equity markets, using daily realized measures data from the Oxford-Man Realized Library, and two widely employed empirical models for realized volatility...

We evaluate forecasts for the euro area in data-rich and ‘data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers' Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data...

ARFIMA models, as advocated by Jiang and Tian for use in long-term volatility forecasting, are found in a follow-up empirical study to be dominated by a certain simple historical predictor of stock price volatility at a five-year horizon. (This particular historical predictor is not recommended...

We evaluate forecasts for the euro area in data-rich and ‘data-lean’ environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus...

We adopt Extreme Gradient Boosting (XGBoost) to forecast realized volatility. This is motivated by XGBoost's strong forecasting performance in other forecast applications and its ability to capture non-linearities, a feature that is also frequently reported in the context of realized volatility....

We evaluate forecasts for the euro area in data-rich and 'data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data...